{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Well_Test_Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO0zWzFma6dLhxRDGyjb8Cl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/Well_Test_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vc6sNWulAyJu",
        "colab_type": "text"
      },
      "source": [
        "#Pressure Drawdown Testing with Python\n",
        "\n",
        "Author : Divyanshu Vyas (PE | RE | Machine Learning/Deep Learning)\n",
        "Email : dvyas13ad@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4HPdxP0BA9A",
        "colab_type": "text"
      },
      "source": [
        "##Problem Statement :\n",
        "\n",
        "![image.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlU2jFOIAb8n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZhGvdzxAqcF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Input Data \n",
        "\n",
        "q = 250 #stb/d\n",
        "\n",
        "tp = 460 #hrs\n",
        "\n",
        "Pi = 4412 #psia\n",
        "\n",
        "h = 69 #ft\n",
        "\n",
        "phi = 3.9/100\n",
        "\n",
        "ct = 17E-6 #psi-1\n",
        "\n",
        "B = 1.136 #rb/stb\n",
        "\n",
        "mu = 0.8 #cp\n",
        "\n",
        "rw = 0.198 #ft\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Acoj8psvCJiH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Pwf v/s t\n",
        "\n",
        "df = pd.read_excel('WTA.xlsx')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksYJ7k75DF1q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['delP'] = Pi - df['Pwf']"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC2yddBxDGvt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = np.array(df['t'])\n",
        "\n",
        "dt = []\n",
        "for i in range(len(t)):\n",
        "\n",
        "  if i==0:\n",
        "    dt.append(t[i])\n",
        "  else:\n",
        "    dt.append(t[i] - t[i-1])\n",
        "\n",
        "dt = np.array(dt)\n"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ullXlTs4D92b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['dt'] = dt"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrgnJnecERp5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = np.array(df['delP'])\n",
        "\n",
        "dp = []\n",
        "for j in range(len(p)):\n",
        "\n",
        "  if j==0:\n",
        "    dp.append(p[j])\n",
        "  else:\n",
        "    dp.append(p[j] - p[j-1])\n",
        "\n",
        "dp = np.array(dp)\n"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVJl8cPXGbCV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['dP'] = dp"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waTQU15PHDOz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9deead2d-a2e6-414e-f57b-f8e36a6e89ce"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>dt</th>\n",
              "      <th>dP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00</td>\n",
              "      <td>4412</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.60</td>\n",
              "      <td>3812</td>\n",
              "      <td>600</td>\n",
              "      <td>1.60</td>\n",
              "      <td>600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.94</td>\n",
              "      <td>3699</td>\n",
              "      <td>713</td>\n",
              "      <td>0.34</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.79</td>\n",
              "      <td>3653</td>\n",
              "      <td>759</td>\n",
              "      <td>0.85</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.01</td>\n",
              "      <td>3636</td>\n",
              "      <td>776</td>\n",
              "      <td>1.22</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      t   Pwf  delP    dt   dP\n",
              "0  0.00  4412     0  0.00    0\n",
              "1  1.60  3812   600  1.60  600\n",
              "2  1.94  3699   713  0.34  113\n",
              "3  2.79  3653   759  0.85   46\n",
              "4  4.01  3636   776  1.22   17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdhu0QuzIoMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_dash = (df['t'][1:])*(df['dP'][1:])/df['dt'][1:]"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHXtUjSXJKTk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_dash\n",
        "\n",
        "P_del = df['delP'][1:]"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eh-dh8IqJRwJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "a29d6d30-5098-4cac-a01f-dde7e219cb10"
      },
      "source": [
        "plt.style.use('default')\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.loglog(df['t'][1:], P_dash,linestyle='--', marker='o', color='b',label='Derivative Plot')\n",
        "\n",
        "plt.loglog(df['t'][1:], P_del,linestyle='--', marker='o', color='r',label='Drawdown')\n",
        "\n",
        "plt.title('Log-Log Plot (diagnostic Plot)')\n",
        "\n",
        "plt.xlabel('Time, hrs')\n",
        "plt.ylabel(\"delP and (delP)'\")\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.grid()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIO1uB5xJkHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We can clearly see that the IARF Exists for roughly around t=10 to t = 100 hours"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHjCPhFRL1Tg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_IARF = df[(df['t']>=10) & (df['t']<=100)]"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ecb-p9KMC_C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "4733d271-23cf-4a3a-b968-971e1cb03544"
      },
      "source": [
        "df_IARF"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>dt</th>\n",
              "      <th>dP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>14.4</td>\n",
              "      <td>3573</td>\n",
              "      <td>839</td>\n",
              "      <td>4.41</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17.3</td>\n",
              "      <td>3567</td>\n",
              "      <td>845</td>\n",
              "      <td>2.90</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>20.7</td>\n",
              "      <td>3561</td>\n",
              "      <td>851</td>\n",
              "      <td>3.40</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>24.9</td>\n",
              "      <td>3555</td>\n",
              "      <td>857</td>\n",
              "      <td>4.20</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>29.8</td>\n",
              "      <td>3549</td>\n",
              "      <td>863</td>\n",
              "      <td>4.90</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>35.8</td>\n",
              "      <td>3544</td>\n",
              "      <td>868</td>\n",
              "      <td>6.00</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>43.0</td>\n",
              "      <td>3537</td>\n",
              "      <td>875</td>\n",
              "      <td>7.20</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>51.5</td>\n",
              "      <td>3532</td>\n",
              "      <td>880</td>\n",
              "      <td>8.50</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>61.8</td>\n",
              "      <td>3526</td>\n",
              "      <td>886</td>\n",
              "      <td>10.30</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>74.2</td>\n",
              "      <td>3521</td>\n",
              "      <td>891</td>\n",
              "      <td>12.40</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>89.1</td>\n",
              "      <td>3515</td>\n",
              "      <td>897</td>\n",
              "      <td>14.90</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t   Pwf  delP     dt  dP\n",
              "10  14.4  3573   839   4.41  13\n",
              "11  17.3  3567   845   2.90   6\n",
              "12  20.7  3561   851   3.40   6\n",
              "13  24.9  3555   857   4.20   6\n",
              "14  29.8  3549   863   4.90   6\n",
              "15  35.8  3544   868   6.00   5\n",
              "16  43.0  3537   875   7.20   7\n",
              "17  51.5  3532   880   8.50   5\n",
              "18  61.8  3526   886  10.30   6\n",
              "19  74.2  3521   891  12.40   5\n",
              "20  89.1  3515   897  14.90   6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QwP_CGRMEu4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d16752e0-cc44-436a-843e-0eeab346e099"
      },
      "source": [
        "#Lets Plot a Semilog Plot for this data now. \n",
        "\n",
        "\n",
        "df_IARF['logt'] = np.log10(df_IARF['t'])"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvO28SiAMdZd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "3ac8f944-8344-43a6-fbe2-fea48c9388b4"
      },
      "source": [
        "df_IARF"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>dt</th>\n",
              "      <th>dP</th>\n",
              "      <th>logt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>14.4</td>\n",
              "      <td>3573</td>\n",
              "      <td>839</td>\n",
              "      <td>4.41</td>\n",
              "      <td>13</td>\n",
              "      <td>1.158362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17.3</td>\n",
              "      <td>3567</td>\n",
              "      <td>845</td>\n",
              "      <td>2.90</td>\n",
              "      <td>6</td>\n",
              "      <td>1.238046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>20.7</td>\n",
              "      <td>3561</td>\n",
              "      <td>851</td>\n",
              "      <td>3.40</td>\n",
              "      <td>6</td>\n",
              "      <td>1.315970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>24.9</td>\n",
              "      <td>3555</td>\n",
              "      <td>857</td>\n",
              "      <td>4.20</td>\n",
              "      <td>6</td>\n",
              "      <td>1.396199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>29.8</td>\n",
              "      <td>3549</td>\n",
              "      <td>863</td>\n",
              "      <td>4.90</td>\n",
              "      <td>6</td>\n",
              "      <td>1.474216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>35.8</td>\n",
              "      <td>3544</td>\n",
              "      <td>868</td>\n",
              "      <td>6.00</td>\n",
              "      <td>5</td>\n",
              "      <td>1.553883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>43.0</td>\n",
              "      <td>3537</td>\n",
              "      <td>875</td>\n",
              "      <td>7.20</td>\n",
              "      <td>7</td>\n",
              "      <td>1.633468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>51.5</td>\n",
              "      <td>3532</td>\n",
              "      <td>880</td>\n",
              "      <td>8.50</td>\n",
              "      <td>5</td>\n",
              "      <td>1.711807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>61.8</td>\n",
              "      <td>3526</td>\n",
              "      <td>886</td>\n",
              "      <td>10.30</td>\n",
              "      <td>6</td>\n",
              "      <td>1.790988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>74.2</td>\n",
              "      <td>3521</td>\n",
              "      <td>891</td>\n",
              "      <td>12.40</td>\n",
              "      <td>5</td>\n",
              "      <td>1.870404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>89.1</td>\n",
              "      <td>3515</td>\n",
              "      <td>897</td>\n",
              "      <td>14.90</td>\n",
              "      <td>6</td>\n",
              "      <td>1.949878</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t   Pwf  delP     dt  dP      logt\n",
              "10  14.4  3573   839   4.41  13  1.158362\n",
              "11  17.3  3567   845   2.90   6  1.238046\n",
              "12  20.7  3561   851   3.40   6  1.315970\n",
              "13  24.9  3555   857   4.20   6  1.396199\n",
              "14  29.8  3549   863   4.90   6  1.474216\n",
              "15  35.8  3544   868   6.00   5  1.553883\n",
              "16  43.0  3537   875   7.20   7  1.633468\n",
              "17  51.5  3532   880   8.50   5  1.711807\n",
              "18  61.8  3526   886  10.30   6  1.790988\n",
              "19  74.2  3521   891  12.40   5  1.870404\n",
              "20  89.1  3515   897  14.90   6  1.949878"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcUQZtaZMi4S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "3f71aa75-79d8-42b0-fdbe-fa59c8ec3a28"
      },
      "source": [
        "plt.plot(df_IARF['logt'],df_IARF['Pwf'],label='Almost a Perfect Straight line')\n",
        "\n",
        "plt.title('Semi-Log (Conventional Plot) for IARF')\n",
        "\n",
        "plt.xlabel('Time, hrs')\n",
        "plt.ylabel(\"Pwf, Psia\")\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.grid()\n",
        "\n"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfkujU_pMtHN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The slope of this straight line is - m = -162.6*q*mu*B/K/h hence we can find the Permeability. \n",
        "\n",
        "coeffs = np.polyfit(df_IARF['logt'],df_IARF['Pwf'],1)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E9YySPpOAMd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m = coeffs[0]"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoLvRWFiOIL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k = abs(162.6*q*mu*B/m/h)"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saqkS_3vOKOk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "13f0b765-57a5-4370-c567-7a49e20cda12"
      },
      "source": [
        "#Conclusion\n",
        "\n",
        "print(f'--> Hence the Natural Absolute Permeability of the reservoir is: {k} mD.')"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--> Hence the Natural Absolute Permeability of the reservoir is: 7.314556876770051 mD.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykp7g0RfOOcY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}